Overview

Dataset statistics

Number of variables24
Number of observations103904
Missing cells310
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.0 MiB
Average record size in memory192.0 B

Variable types

Numeric18
Categorical6

Alerts

id is uniformly distributedUniform
id has unique valuesUnique
instore_wifi has 3103 (3.0%) zerosZeros
open/close_time_convenient has 5300 (5.1%) zerosZeros
easy_of_online_shopping has 4487 (4.3%) zerosZeros
dressing_room has 2428 (2.3%) zerosZeros
carrier_delay_in_minutes has 58668 (56.5%) zerosZeros
delivery_delay_in_minutes has 58159 (56.0%) zerosZeros

Reproduction

Analysis started2023-12-30 03:13:32.384782
Analysis finished2023-12-30 03:14:05.104880
Duration32.72 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct103904
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64924.211
Minimum1
Maximum129880
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2023-12-30T00:14:05.250902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6593.15
Q132533.75
median64856.5
Q397368.25
95-th percentile123409.7
Maximum129880
Range129879
Interquartile range (IQR)64834.5

Descriptive statistics

Standard deviation37463.812
Coefficient of variation (CV)0.57703917
Kurtosis-1.1984401
Mean64924.211
Median Absolute Deviation (MAD)32410
Skewness0.0028642483
Sum6.7458852 × 109
Variance1.4035372 × 109
MonotonicityNot monotonic
2023-12-30T00:14:05.370131image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70172 1
 
< 0.1%
116739 1
 
< 0.1%
6259 1
 
< 0.1%
17470 1
 
< 0.1%
118574 1
 
< 0.1%
23529 1
 
< 0.1%
16272 1
 
< 0.1%
58438 1
 
< 0.1%
2352 1
 
< 0.1%
65908 1
 
< 0.1%
Other values (103894) 103894
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
129880 1
< 0.1%
129879 1
< 0.1%
129878 1
< 0.1%
129875 1
< 0.1%
129874 1
< 0.1%
129873 1
< 0.1%
129871 1
< 0.1%
129870 1
< 0.1%
129869 1
< 0.1%
129867 1
< 0.1%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size811.9 KiB
0
52727 
1
51177 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters103904
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 52727
50.7%
1 51177
49.3%

Length

2023-12-30T00:14:05.474673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-30T00:14:05.574670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 52727
50.7%
1 51177
49.3%

Most occurring characters

ValueCountFrequency (%)
0 52727
50.7%
1 51177
49.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 103904
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 52727
50.7%
1 51177
49.3%

Most occurring scripts

ValueCountFrequency (%)
Common 103904
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 52727
50.7%
1 51177
49.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 52727
50.7%
1 51177
49.3%

customer_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size811.9 KiB
0
84923 
1
18981 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters103904
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 84923
81.7%
1 18981
 
18.3%

Length

2023-12-30T00:14:05.654645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-30T00:14:05.726075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 84923
81.7%
1 18981
 
18.3%

Most occurring characters

ValueCountFrequency (%)
0 84923
81.7%
1 18981
 
18.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 103904
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 84923
81.7%
1 18981
 
18.3%

Most occurring scripts

ValueCountFrequency (%)
Common 103904
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 84923
81.7%
1 18981
 
18.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 84923
81.7%
1 18981
 
18.3%

age
Real number (ℝ)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.379706
Minimum7
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2023-12-30T00:14:05.815983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14
Q127
median40
Q351
95-th percentile64
Maximum85
Range78
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.114964
Coefficient of variation (CV)0.38382622
Kurtosis-0.71956812
Mean39.379706
Median Absolute Deviation (MAD)12
Skewness-0.0045161271
Sum4091709
Variance228.46213
MonotonicityNot monotonic
2023-12-30T00:14:05.924986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 2969
 
2.9%
25 2798
 
2.7%
40 2574
 
2.5%
44 2482
 
2.4%
42 2457
 
2.4%
41 2456
 
2.4%
22 2351
 
2.3%
23 2346
 
2.3%
45 2339
 
2.3%
47 2329
 
2.2%
Other values (65) 78803
75.8%
ValueCountFrequency (%)
7 562
0.5%
8 640
0.6%
9 692
0.7%
10 683
0.7%
11 678
0.7%
12 635
0.6%
13 633
0.6%
14 707
0.7%
15 818
0.8%
16 899
0.9%
ValueCountFrequency (%)
85 17
 
< 0.1%
80 78
 
0.1%
79 42
 
< 0.1%
78 33
 
< 0.1%
77 87
0.1%
76 45
 
< 0.1%
75 61
 
0.1%
74 47
 
< 0.1%
73 51
 
< 0.1%
72 201
0.2%

type_of_purchase
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size811.9 KiB
0
71655 
1
32249 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters103904
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 71655
69.0%
1 32249
31.0%

Length

2023-12-30T00:14:06.031462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-30T00:14:06.104994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 71655
69.0%
1 32249
31.0%

Most occurring characters

ValueCountFrequency (%)
0 71655
69.0%
1 32249
31.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 103904
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 71655
69.0%
1 32249
31.0%

Most occurring scripts

ValueCountFrequency (%)
Common 103904
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 71655
69.0%
1 32249
31.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 71655
69.0%
1 32249
31.0%

store_size
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size811.9 KiB
0
49665 
1
46745 
2
7494 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters103904
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 49665
47.8%
1 46745
45.0%
2 7494
 
7.2%

Length

2023-12-30T00:14:06.189041image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-30T00:14:06.264882image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49665
47.8%
1 46745
45.0%
2 7494
 
7.2%

Most occurring characters

ValueCountFrequency (%)
0 49665
47.8%
1 46745
45.0%
2 7494
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 103904
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49665
47.8%
1 46745
45.0%
2 7494
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Common 103904
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49665
47.8%
1 46745
45.0%
2 7494
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 49665
47.8%
1 46745
45.0%
2 7494
 
7.2%

store_distance
Real number (ℝ)

Distinct3802
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1189.4484
Minimum31
Maximum4983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2023-12-30T00:14:06.360008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile175
Q1414
median843
Q31743
95-th percentile3383
Maximum4983
Range4952
Interquartile range (IQR)1329

Descriptive statistics

Standard deviation997.14728
Coefficient of variation (CV)0.8383275
Kurtosis0.26853544
Mean1189.4484
Median Absolute Deviation (MAD)517
Skewness1.1094657
Sum1.2358844 × 108
Variance994302.7
MonotonicityNot monotonic
2023-12-30T00:14:06.469895image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
337 660
 
0.6%
594 395
 
0.4%
404 392
 
0.4%
862 369
 
0.4%
2475 369
 
0.4%
447 362
 
0.3%
236 351
 
0.3%
192 333
 
0.3%
399 332
 
0.3%
308 329
 
0.3%
Other values (3792) 100012
96.3%
ValueCountFrequency (%)
31 8
 
< 0.1%
56 8
 
< 0.1%
67 128
0.1%
73 59
0.1%
74 30
 
< 0.1%
76 1
 
< 0.1%
77 41
 
< 0.1%
78 30
 
< 0.1%
80 2
 
< 0.1%
82 7
 
< 0.1%
ValueCountFrequency (%)
4983 12
< 0.1%
4963 13
< 0.1%
4817 5
 
< 0.1%
4502 10
< 0.1%
4243 18
< 0.1%
4000 11
< 0.1%
3999 5
 
< 0.1%
3998 8
< 0.1%
3997 9
< 0.1%
3996 8
< 0.1%

instore_wifi
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7296832
Minimum0
Maximum5
Zeros3103
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2023-12-30T00:14:06.560505image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3278295
Coefficient of variation (CV)0.48644088
Kurtosis-0.84616972
Mean2.7296832
Median Absolute Deviation (MAD)1
Skewness0.040408022
Sum283625
Variance1.7631311
MonotonicityNot monotonic
2023-12-30T00:14:06.637433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 25868
24.9%
2 25830
24.9%
4 19794
19.1%
1 17840
17.2%
5 11469
11.0%
0 3103
 
3.0%
ValueCountFrequency (%)
0 3103
 
3.0%
1 17840
17.2%
2 25830
24.9%
3 25868
24.9%
4 19794
19.1%
5 11469
11.0%
ValueCountFrequency (%)
5 11469
11.0%
4 19794
19.1%
3 25868
24.9%
2 25830
24.9%
1 17840
17.2%
0 3103
 
3.0%

open/close_time_convenient
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.060296
Minimum0
Maximum5
Zeros5300
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2023-12-30T00:14:06.720081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5250752
Coefficient of variation (CV)0.49834237
Kurtosis-1.0377673
Mean3.060296
Median Absolute Deviation (MAD)1
Skewness-0.33439863
Sum317977
Variance2.3258544
MonotonicityNot monotonic
2023-12-30T00:14:06.804632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 25546
24.6%
5 22403
21.6%
3 17966
17.3%
2 17191
16.5%
1 15498
14.9%
0 5300
 
5.1%
ValueCountFrequency (%)
0 5300
 
5.1%
1 15498
14.9%
2 17191
16.5%
3 17966
17.3%
4 25546
24.6%
5 22403
21.6%
ValueCountFrequency (%)
5 22403
21.6%
4 25546
24.6%
3 17966
17.3%
2 17191
16.5%
1 15498
14.9%
0 5300
 
5.1%

easy_of_online_shopping
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7569006
Minimum0
Maximum5
Zeros4487
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2023-12-30T00:14:06.884737image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3989295
Coefficient of variation (CV)0.50742833
Kurtosis-0.91034621
Mean2.7569006
Median Absolute Deviation (MAD)1
Skewness-0.018294273
Sum286453
Variance1.9570037
MonotonicityNot monotonic
2023-12-30T00:14:06.965803image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 24449
23.5%
2 24021
23.1%
4 19571
18.8%
1 17525
16.9%
5 13851
13.3%
0 4487
 
4.3%
ValueCountFrequency (%)
0 4487
 
4.3%
1 17525
16.9%
2 24021
23.1%
3 24449
23.5%
4 19571
18.8%
5 13851
13.3%
ValueCountFrequency (%)
5 13851
13.3%
4 19571
18.8%
3 24449
23.5%
2 24021
23.1%
1 17525
16.9%
0 4487
 
4.3%

store_location
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9768825
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2023-12-30T00:14:07.044526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.277621
Coefficient of variation (CV)0.42918087
Kurtosis-1.0302833
Mean2.9768825
Median Absolute Deviation (MAD)1
Skewness-0.058889412
Sum309310
Variance1.6323154
MonotonicityNot monotonic
2023-12-30T00:14:07.124963image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 28577
27.5%
4 24426
23.5%
2 19459
18.7%
1 17562
16.9%
5 13879
13.4%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 17562
16.9%
2 19459
18.7%
3 28577
27.5%
4 24426
23.5%
5 13879
13.4%
ValueCountFrequency (%)
5 13879
13.4%
4 24426
23.5%
3 28577
27.5%
2 19459
18.7%
1 17562
16.9%
0 1
 
< 0.1%

toilet_cleaning
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2021289
Minimum0
Maximum5
Zeros107
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2023-12-30T00:14:07.205304image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3295327
Coefficient of variation (CV)0.41520275
Kurtosis-1.1454532
Mean3.2021289
Median Absolute Deviation (MAD)1
Skewness-0.1512795
Sum332714
Variance1.7676572
MonotonicityNot monotonic
2023-12-30T00:14:07.285765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 24359
23.4%
5 22313
21.5%
3 22300
21.5%
2 21988
21.2%
1 12837
12.4%
0 107
 
0.1%
ValueCountFrequency (%)
0 107
 
0.1%
1 12837
12.4%
2 21988
21.2%
3 22300
21.5%
4 24359
23.4%
5 22313
21.5%
ValueCountFrequency (%)
5 22313
21.5%
4 24359
23.4%
3 22300
21.5%
2 21988
21.2%
1 12837
12.4%
0 107
 
0.1%

dressing_room
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2503753
Minimum0
Maximum5
Zeros2428
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2023-12-30T00:14:07.369548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.349509
Coefficient of variation (CV)0.41518557
Kurtosis-0.7020058
Mean3.2503753
Median Absolute Deviation (MAD)1
Skewness-0.4538517
Sum337727
Variance1.8211744
MonotonicityNot monotonic
2023-12-30T00:14:07.454801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 30762
29.6%
3 21804
21.0%
5 20713
19.9%
2 17505
16.8%
1 10692
 
10.3%
0 2428
 
2.3%
ValueCountFrequency (%)
0 2428
 
2.3%
1 10692
 
10.3%
2 17505
16.8%
3 21804
21.0%
4 30762
29.6%
5 20713
19.9%
ValueCountFrequency (%)
5 20713
19.9%
4 30762
29.6%
3 21804
21.0%
2 17505
16.8%
1 10692
 
10.3%
0 2428
 
2.3%

waiting_room
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.439396
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2023-12-30T00:14:08.134960image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.3190875
Coefficient of variation (CV)0.38352302
Kurtosis-0.92570207
Mean3.439396
Median Absolute Deviation (MAD)1
Skewness-0.48277539
Sum357367
Variance1.7399919
MonotonicityNot monotonic
2023-12-30T00:14:08.218091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 31765
30.6%
5 26470
25.5%
3 18696
18.0%
2 14897
14.3%
1 12075
 
11.6%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 12075
 
11.6%
2 14897
14.3%
3 18696
18.0%
4 31765
30.6%
5 26470
25.5%
ValueCountFrequency (%)
5 26470
25.5%
4 31765
30.6%
3 18696
18.0%
2 14897
14.3%
1 12075
 
11.6%
0 1
 
< 0.1%

kids_entertainment
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3581575
Minimum0
Maximum5
Zeros14
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2023-12-30T00:14:08.304530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3329907
Coefficient of variation (CV)0.39694109
Kurtosis-1.0606958
Mean3.3581575
Median Absolute Deviation (MAD)1
Skewness-0.36513059
Sum348926
Variance1.7768642
MonotonicityNot monotonic
2023-12-30T00:14:08.386844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 29423
28.3%
5 25213
24.3%
3 19139
18.4%
2 17637
17.0%
1 12478
12.0%
0 14
 
< 0.1%
ValueCountFrequency (%)
0 14
 
< 0.1%
1 12478
12.0%
2 17637
17.0%
3 19139
18.4%
4 29423
28.3%
5 25213
24.3%
ValueCountFrequency (%)
5 25213
24.3%
4 29423
28.3%
3 19139
18.4%
2 17637
17.0%
1 12478
12.0%
0 14
 
< 0.1%

seller_service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3823626
Minimum0
Maximum5
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2023-12-30T00:14:08.471973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2883544
Coefficient of variation (CV)0.38090368
Kurtosis-0.89233524
Mean3.3823626
Median Absolute Deviation (MAD)1
Skewness-0.42003075
Sum351441
Variance1.659857
MonotonicityNot monotonic
2023-12-30T00:14:08.554793image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 30867
29.7%
5 23648
22.8%
3 22833
22.0%
2 14681
14.1%
1 11872
 
11.4%
0 3
 
< 0.1%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 11872
 
11.4%
2 14681
14.1%
3 22833
22.0%
4 30867
29.7%
5 23648
22.8%
ValueCountFrequency (%)
5 23648
22.8%
4 30867
29.7%
3 22833
22.0%
2 14681
14.1%
1 11872
 
11.4%
0 3
 
< 0.1%

showroom
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3510548
Minimum0
Maximum5
Zeros472
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2023-12-30T00:14:08.644885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3156046
Coefficient of variation (CV)0.39259418
Kurtosis-0.98025691
Mean3.3510548
Median Absolute Deviation (MAD)1
Skewness-0.35023134
Sum348188
Variance1.7308155
MonotonicityNot monotonic
2023-12-30T00:14:08.734330image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 28789
27.7%
5 24667
23.7%
3 20098
19.3%
2 19525
18.8%
1 10353
 
10.0%
0 472
 
0.5%
ValueCountFrequency (%)
0 472
 
0.5%
1 10353
 
10.0%
2 19525
18.8%
3 20098
19.3%
4 28789
27.7%
5 24667
23.7%
ValueCountFrequency (%)
5 24667
23.7%
4 28789
27.7%
3 20098
19.3%
2 19525
18.8%
1 10353
 
10.0%
0 472
 
0.5%

self-store
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size811.9 KiB
4
37383 
5
27131 
3
20632 
2
11521 
1
7237 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters103904
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row4
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 37383
36.0%
5 27131
26.1%
3 20632
19.9%
2 11521
 
11.1%
1 7237
 
7.0%

Length

2023-12-30T00:14:08.827837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-30T00:14:08.917591image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
4 37383
36.0%
5 27131
26.1%
3 20632
19.9%
2 11521
 
11.1%
1 7237
 
7.0%

Most occurring characters

ValueCountFrequency (%)
4 37383
36.0%
5 27131
26.1%
3 20632
19.9%
2 11521
 
11.1%
1 7237
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 103904
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 37383
36.0%
5 27131
26.1%
3 20632
19.9%
2 11521
 
11.1%
1 7237
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
Common 103904
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 37383
36.0%
5 27131
26.1%
3 20632
19.9%
2 11521
 
11.1%
1 7237
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 37383
36.0%
5 27131
26.1%
3 20632
19.9%
2 11521
 
11.1%
1 7237
 
7.0%

purchase_service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3042905
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2023-12-30T00:14:09.004791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2653958
Coefficient of variation (CV)0.38295538
Kurtosis-0.82887706
Mean3.3042905
Median Absolute Deviation (MAD)1
Skewness-0.36498196
Sum343329
Variance1.6012266
MonotonicityNot monotonic
2023-12-30T00:14:09.107421image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 29055
28.0%
3 28446
27.4%
5 20619
19.8%
2 12893
12.4%
1 12890
12.4%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 12890
12.4%
2 12893
12.4%
3 28446
27.4%
4 29055
28.0%
5 20619
19.8%
ValueCountFrequency (%)
5 20619
19.8%
4 29055
28.0%
3 28446
27.4%
2 12893
12.4%
1 12890
12.4%
0 1
 
< 0.1%

store_service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6404277
Minimum0
Maximum5
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2023-12-30T00:14:09.189477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.175663
Coefficient of variation (CV)0.3229464
Kurtosis-0.3575092
Mean3.6404277
Median Absolute Deviation (MAD)1
Skewness-0.69031396
Sum378255
Variance1.3821836
MonotonicityNot monotonic
2023-12-30T00:14:09.272995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 37945
36.5%
5 27116
26.1%
3 20299
19.5%
2 11457
 
11.0%
1 7084
 
6.8%
0 3
 
< 0.1%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 7084
 
6.8%
2 11457
 
11.0%
3 20299
19.5%
4 37945
36.5%
5 27116
26.1%
ValueCountFrequency (%)
5 27116
26.1%
4 37945
36.5%
3 20299
19.5%
2 11457
 
11.0%
1 7084
 
6.8%
0 3
 
< 0.1%

cleanliness
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2863509
Minimum0
Maximum5
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2023-12-30T00:14:09.355085image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3122728
Coefficient of variation (CV)0.39931003
Kurtosis-1.0125577
Mean3.2863509
Median Absolute Deviation (MAD)1
Skewness-0.30007449
Sum341465
Variance1.72206
MonotonicityNot monotonic
2023-12-30T00:14:09.437466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 27179
26.2%
3 24574
23.7%
5 22689
21.8%
2 16132
15.5%
1 13318
12.8%
0 12
 
< 0.1%
ValueCountFrequency (%)
0 12
 
< 0.1%
1 13318
12.8%
2 16132
15.5%
3 24574
23.7%
4 27179
26.2%
5 22689
21.8%
ValueCountFrequency (%)
5 22689
21.8%
4 27179
26.2%
3 24574
23.7%
2 16132
15.5%
1 13318
12.8%
0 12
 
< 0.1%

carrier_delay_in_minutes
Real number (ℝ)

ZEROS 

Distinct446
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.815618
Minimum0
Maximum1592
Zeros58668
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2023-12-30T00:14:09.531884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile78
Maximum1592
Range1592
Interquartile range (IQR)12

Descriptive statistics

Standard deviation38.230901
Coefficient of variation (CV)2.5804458
Kurtosis100.26701
Mean14.815618
Median Absolute Deviation (MAD)0
Skewness6.7339795
Sum1539402
Variance1461.6018
MonotonicityNot monotonic
2023-12-30T00:14:09.644978image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58668
56.5%
1 2948
 
2.8%
2 2274
 
2.2%
3 2009
 
1.9%
4 1854
 
1.8%
5 1692
 
1.6%
6 1517
 
1.5%
7 1392
 
1.3%
8 1295
 
1.2%
9 1255
 
1.2%
Other values (436) 29000
27.9%
ValueCountFrequency (%)
0 58668
56.5%
1 2948
 
2.8%
2 2274
 
2.2%
3 2009
 
1.9%
4 1854
 
1.8%
5 1692
 
1.6%
6 1517
 
1.5%
7 1392
 
1.3%
8 1295
 
1.2%
9 1255
 
1.2%
ValueCountFrequency (%)
1592 1
< 0.1%
1305 1
< 0.1%
1017 1
< 0.1%
978 1
< 0.1%
933 1
< 0.1%
930 1
< 0.1%
921 1
< 0.1%
859 1
< 0.1%
853 1
< 0.1%
750 1
< 0.1%

delivery_delay_in_minutes
Real number (ℝ)

ZEROS 

Distinct455
Distinct (%)0.4%
Missing310
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean15.178678
Minimum0
Maximum1584
Zeros58159
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size811.9 KiB
2023-12-30T00:14:09.756714image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q313
95-th percentile79
Maximum1584
Range1584
Interquartile range (IQR)13

Descriptive statistics

Standard deviation38.698682
Coefficient of variation (CV)2.5495423
Kurtosis94.537006
Mean15.178678
Median Absolute Deviation (MAD)0
Skewness6.5966368
Sum1572420
Variance1497.588
MonotonicityNot monotonic
2023-12-30T00:14:09.871403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58159
56.0%
1 2211
 
2.1%
2 2064
 
2.0%
3 1952
 
1.9%
4 1907
 
1.8%
5 1658
 
1.6%
6 1616
 
1.6%
7 1481
 
1.4%
8 1394
 
1.3%
9 1264
 
1.2%
Other values (445) 29888
28.8%
ValueCountFrequency (%)
0 58159
56.0%
1 2211
 
2.1%
2 2064
 
2.0%
3 1952
 
1.9%
4 1907
 
1.8%
5 1658
 
1.6%
6 1616
 
1.6%
7 1481
 
1.4%
8 1394
 
1.3%
9 1264
 
1.2%
ValueCountFrequency (%)
1584 1
< 0.1%
1280 1
< 0.1%
1011 1
< 0.1%
970 1
< 0.1%
952 1
< 0.1%
924 1
< 0.1%
920 1
< 0.1%
860 1
< 0.1%
823 1
< 0.1%
729 1
< 0.1%

satisfaction
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size811.9 KiB
0
58879 
1
45025 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters103904
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 58879
56.7%
1 45025
43.3%

Length

2023-12-30T00:14:09.974838image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-30T00:14:10.048836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 58879
56.7%
1 45025
43.3%

Most occurring characters

ValueCountFrequency (%)
0 58879
56.7%
1 45025
43.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 103904
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 58879
56.7%
1 45025
43.3%

Most occurring scripts

ValueCountFrequency (%)
Common 103904
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 58879
56.7%
1 45025
43.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 58879
56.7%
1 45025
43.3%

Interactions

2023-12-30T00:14:02.728057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:34.275089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:36.123859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:38.134213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:39.925244image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:41.856112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:43.391572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:44.952420image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:46.884788image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:48.404703image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:49.991112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:51.494867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:53.261693image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:54.706413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:56.175175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:57.674678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:59.671165image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:14:01.205509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:14:02.814654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:34.397127image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:36.233809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:38.240157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:40.026061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:41.938482image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:43.480107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:45.037194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2023-12-30T00:13:46.042966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:47.884862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:49.479236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:51.002177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:52.769807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:54.221973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:55.675262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:57.150900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:58.686990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:14:00.668718image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:14:02.230013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:14:03.846127image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:35.574169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:37.601245image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:39.399256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:41.190465image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:42.959241image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:44.491888image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:46.149819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:47.972093image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:49.564510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:51.077721image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:52.844632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:54.294562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:55.754571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:57.232939image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:59.227363image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:14:00.749442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:14:02.307595image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:14:03.924911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:35.675214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:37.700548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:39.498369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:41.291831image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:43.041748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:44.586841image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:46.521919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:48.049622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:49.637140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:51.159839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:52.924820image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:54.376239image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:55.833912image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:57.312507image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:59.309354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:14:00.834574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:14:02.391746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:14:04.011310image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:35.787630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:37.805496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:39.597245image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:41.383528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:43.125062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:44.668711image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:46.608073image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:48.135513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:49.724573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:51.235711image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:53.006392image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:54.453556image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:55.906514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:57.384910image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:59.396412image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:14:00.914659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:14:02.474859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:14:04.104595image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:35.897709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:37.911635image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:39.700229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:41.468977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:43.205452image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:44.758353image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:46.686348image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:48.219711image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:49.804528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:51.317879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:53.086785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:54.531242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:55.989120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:57.467950image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:59.480315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:14:00.996786image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:14:02.554560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:14:04.204915image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:36.012157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:38.028286image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:39.818630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:41.765661image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:43.302125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:44.856408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:46.786020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:48.311451image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:49.902223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:51.404507image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:53.170928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:54.625071image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:56.083620image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:57.557582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:13:59.574940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:14:01.102467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-30T00:14:02.639751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2023-12-30T00:14:04.360335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-30T00:14:04.714547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idgendercustomer_typeagetype_of_purchasestore_sizestore_distanceinstore_wifiopen/close_time_convenienteasy_of_online_shoppingstore_locationtoilet_cleaningdressing_roomwaiting_roomkids_entertainmentseller_serviceshowroomself-storepurchase_servicestore_servicecleanlinesscarrier_delay_in_minutesdelivery_delay_in_minutessatisfaction
070172101312460343153554344552518.00
150471125002353233131115314116.00
211002800260011422222555543444500.01
32402600250056225552222253142119.00
41192991061002143333455334433300.01
511115700261111803421121134444100.00
682113104711127624232222334352923.00
79646200520020354344555555545440.01
8794850041008531222433112141200.00
96572511200110613334233223443200.00
idgendercustomer_typeagetype_of_purchasestore_sizestore_distanceinstore_wifiopen/close_time_convenienteasy_of_online_shoppingstore_locationtoilet_cleaningdressing_roomwaiting_roomkids_entertainmentseller_serviceshowroomself-storepurchase_servicestore_servicecleanlinesscarrier_delay_in_minutesdelivery_delay_in_minutessatisfaction
10389486549102600712444455553443451726.01
103895660300124011055111211113355411310.00
103896714451057018674555444434313400.00
10389710220300600015995555554444444497.01
1038986066610501116203134232243424200.00
103899941710123011922123222231423230.00
1039007309710490023474444245555555400.01
10390168825113000199511134154324554714.00
1039025417301220110001115111145154100.00
1039036256710270017231333111111443100.00